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						<h2>SpeechBrain Tutorials</h2>
						<h3>SpeechBrain Advanced</h3>

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          			<p> <img src="img/logo_discourse.png"/> Join our official <a href="https://speechbrain.discourse.group">Discourse</a> to discuss with SpeechBrain users coming from all around the world! <img src="img/logo_discourse.png"/></p>
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                                          <a class="active" href="#">SpeechBrain Advanced</a>
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                                          <li><a href="about.html">Cornell S. & Parcollet T.<i class="lnr lnr-user"></i></a></li>
                                          <li><a href="#">Mar. 2021<i class="lnr lnr-calendar-full"></i></a></li>
                                          <li><a href="#">Difficulty: medium<i class="lnr lnr-cog"></i></a></li>
                                          <li><a href="#">Time: 30min<i class="lnr lnr-hourglass"></i></a></li>
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                                        <h2>Pre-trained Models and Fine-Tuning with <img src="img/hf.ico" alt="drawing" width="40" style="box-shadow: 0px 0px 0px -0px rgb(255, 255, 255)"/></h2>
                                        <p>Training DNN models is often very time-consuming and expensive.
                                          For this reason, whenever it is possible, using off-the-shelf pretrained
                                          models can be convenient in various scenarios.
                                          We provide a simple and straightforward way to download and instantiate a
                                          state-of-the-art pretrained-model from <img src="img/hf.ico" alt="drawing" width="20"/> HuggingFace <img src="img/hf.ico" alt="drawing" width="20"/> and use it either for direct inference or
                                          or fine-tuning/knowledge distillation or whatever new fancy technique you can come up with!</p>
                                        <a href="https://colab.research.google.com/drive/1LN7R3U3xneDgDRK2gC5MzGkLysCWxuC3?usp=sharing" class="blog_btn">Open in Google Colab</a>
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                                          <a class="active" href="#">SpeechBrain Advanced</a>
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                                      <ul class="blog_meta list">
                                          <li><a href="about.html">Heba A. & Parcollet T.<i class="lnr lnr-user"></i></a></li>
                                          <li><a href="#">Feb. 2021<i class="lnr lnr-calendar-full"></i></a></li>
                                          <li><a href="#">Difficulty: easy<i class="lnr lnr-cog"></i></a></li>
                                          <li><a href="#">Time: 20min<i class="lnr lnr-hourglass"></i></a></li>
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                                        <h2>Text Tokenizer</h2>
                                        <p>Machine Learning tasks that process text may contain thousands of vocabulary
                                          words which leads to models dealing with huge embeddings as input/output
                                          (e.g. for one-hot-vectors and ndim=vocabulary_size). This causes an important consumption of memory,
                                          complexe computations, and more importantly, sub-optimal learning due to extremely sparse and cumbersome
                                          one-hot vectors. In this tutorial, we provide all the basics needed to correctly use the SpeechBrain Tokenizer relying
                                          on SentencePiece (BPE and unigram).</p>
                                        <a href="https://colab.research.google.com/drive/12yE3myHSH-eUxzNM0-FLtEOhzdQoLYWe?usp=sharing" class="blog_btn">Open in Google Colab</a>
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